A Bio-Inspired-Based Salp Swarm Algorithm Enabled with Deep Learning for Alzheimer’s Classification

Alzheimer’s disease is a progressive neurodegenerative disorder for which early identification is of paramount importance for a holistic treatment plan. Traditional methods of diagnosis require extensive manual interventions, making their scalability and reproducibility difficult. This paper presents a novel Bio-Inspired Salp Swarm Algorithm (BI-SSA) technique enabled by Deep Learning for the classification of Alzheimer’s disease. The social behavior of birds and insects served as inspiration for the optimization technique known as BI-SSA which is able to identify useful solutions to complex problems with minimum manual interventions. This paper extends BI-SSA using Deep Learning which enables it to generate a more accurate and reliable diagnostic model. The model incorporates Alzheimer’s disease-specific features such as age, gender, family history, and cognitive tests and employs an ensemble approach to improve the accuracy of the model. The proposed model is evaluated using a publicly available ADNI dataset. The results demonstrate that the model is able to correctly classify AD patients with an accuracy of 99.9%. Furthermore, our BI-SSA-based model outperforms traditional machine learning techniques and achieves better results with respect to sensitivity, precision, and accuracy of classification.